Search

Alzheimer’s disease (AD) involves a gradual breakdown of brain connectivity, and network analyses offer a promising new approach to track and understand disease progression. Even so, our ability to detect degenerative changes in brain networks depends on the methods used. Here we compared several tractography and feature extraction methods to see which ones gave best diagnostic classification for 202 people with AD, mild cognitive impairment (MCI) or normal cognition, scanned with 41-gradient diffusion-weighted MRI as part of the ADNI project. We computed brain networks based on whole brain tractography with 9 different methods – 4 of them tensor-based deterministic (FACT, RK2, SL, and TL), two ODF-based deterministic (FACT, RK2), two ODF-based probabilistic approaches (Hough and PICo) and one ball-and-stick approach (Probtrackx). Brain networks derived from different tractography algorithms did not differ in terms of classification performance on ADNI, but performing PCA on networks helped classification in some cases. Small differences may still be detectable in a truly vast cohort, but these experiments help assess the relative advantages of different tractography algorithms, and different post-processing choices, when used for classification.